AIMC Topic: Meningeal Neoplasms
Clear Filters Showing 61 to 70 of 71 articles
eNAL++: a new and effective off-line correction protocol for rotational setup errors when using a robotic couch.
Journal of applied clinical medical physics
Nov 8, 2015
Cone-beam CTs (CBCTs) installed on a linear accelerator can be used to provide fast and accurate automatic six degrees of freedom (6DoF) vector displacement information of the patient position just prior to radiotherapy. These displacement correction...
A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 7, 2014
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal ch...
Support Vector Machine
Meningeal Neoplasms
Microscopy
Reproducibility of Results
Humans
Image Interpretation, Computer-Assisted
Sensitivity and Specificity
Pattern Recognition, Automated
Brain Neoplasms
Image Enhancement
Machine Learning
Decision Support Systems, Clinical
Meningioma
Algorithms
Fractals
[Radiosurgery of benign intracranial lesions. Indications, results , and perspectives].
Revue medicale suisse
May 14, 2025
Stereotactic radiosurgery (SRS) is a non-invasive technique that is transforming the management of benign intracranial lesions through its precision and preservation of healthy tissues. It is effective for meningiomas, trigeminal neuralgia (TN), pitu...
An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade.
Scientific reports
May 13, 2025
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas fr...
Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.
NMR in biomedicine
May 1, 2025
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...
Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of meningiomas, such as tumor stiffness or consistency, play a crucial role in the surgical approach. This...
Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.
Neurosurgery
Mar 15, 2021
BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructur...
Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medic...
A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
Spine
May 15, 2020
STUDY DESIGN: Retrospective analysis of magnetic resonance imaging (MRI).
Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.
Journal of digital imaging
Aug 1, 2019
Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to have high accuracy in semantic segmentation of medical (e.g., radiology and pathology) images. However...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 31964
- Neural Networks, Computer 30201
- Algorithms 27375
- Deep Learning 26880
- Artificial Intelligence 24818
- Robotics 13382
- Image Processing, Computer-Assisted 9730
- Retrospective Studies 9328
- Magnetic Resonance Imaging 6341
- Reproducibility of Results 5670
- Tomography, X-Ray Computed 4863
- Support Vector Machine 4762
- Computational Biology 4328
- Brain 4125
- Computer Simulation 3932
Recent Journals
- Scientific reports 6689
- Sensors (Basel, Switzerland) 4794
- PloS one 4186
- Neural networks : the official journal of the International Neural Network Society 3026
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1915
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1378
- IEEE journal of biomedical and health informatics 1167
- Journal of chemical information and modeling 1059
- Nature communications 1029
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 872
- Journal of medical Internet research 853
- Briefings in bioinformatics 853